HiResolution<sup>TM</sup> and Conventional Sound Processing in the HiResolution<sup>TM</sup> Bionic Ear: Using Appropriate Outcome Measures to Assess Speech Recognition Ability
Bibliographic record
Abstract
OBJECTIVE: This study compared speech perception benefits in adults implanted with the HiResolution (HiRes) Bionic Ear who used both conventional and HiRes sound processing. A battery of speech tests was used to determine which formats were most appropriate for documenting the wide range of benefit experienced by cochlear-implant users. STUDY DESIGN: A repeated-measures design was used to assess postimplantation speech perception in adults who received the HiResolution Bionic Ear in a recent clinical trial. Patients were fit first with conventional strategies and assessed after 3 months of use. Patients were then switched to HiRes sound processing and assessed again after 3 months of use. To assess the immediate effect of HiRes sound processing on speech perception performance, consonant recognition testing was performed in a subset of patients after 3 days of HiRes use and compared with their 3-month performance with conventional processing. SETTING: Subjects were implanted and evaluated at 19 cochlear implant programs in the USA and Canada affiliated primarily with tertiary medical centers. PATIENTS: Patients were 51 postlinguistically deafened adults. MAIN OUTCOME MEASURES: Speech perception was assessed using CNC monosyllabic words, CID sentences and HINT sentences in quiet and noise. Consonant recognition testing was also administered to a subset of patients (n = 30) using the Iowa Consonant Test presented in quiet and noise. All patients completed a strategy preference questionnaire after 6 months of device use. RESULTS: Consonant identification in quiet and noise improved significantly after only 3 days of HiRes use. The mean improvement from conventional to HiRes processing was significant on all speech perception tests. The largest differences occurred for the HINT sentences in noise. Ninety-six percent of the patients preferred HiRes to conventional sound processing. Ceiling effects occurred for both sentence tests in quiet. CONCLUSIONS: Although most patients improved after switching to HiRes sound processing, the greatest differences were seen in the 'poor' performers because 'good' performers often reached ceiling performance, especially on tests in quiet. Future evaluations of cochlear-implant benefit should make use of more difficult measures, especially for 'good' users. Nonetheless, a range of difficulty must remain in test materials to document benefit in the entire population of implant recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".